The notion of state estimation (SE) for transmission systems can be traced back to the seventies [1]. Some twenty years later, SE algorithms specifically tailored to distribution systems were introduced [2], [3]. In practice, however, it has not been until very recently that SE tools for distribution feeders have been comprehensively considered [4]-[6]. Smart grid developments are progressively bringing more and more information to Distribution Management Systems (DMS), allowing applications that were long ago conceptually mature but still waiting for the required infrastructure to be deployed at the distribution level [7], [8]. Eventually, the massively distributed nature of medium-voltage and low-voltage subsystems, and the resulting communication bottlenecks, will force utilities to consider some kind of hierarchical organization in today's fully centralized DMS [9]. Indeed, only if raw data are processed in a local manner [10] will it be possible for new and ubiquitous sources of information, such as smart meters and the associated concentrators, to be scanned at rates which are fast enough for real-time network operation. Until this partly decentralized environment arrives, DMS operators can only expect to have once-a-day or few-times-a-day values of energy consumed by customers connected to the distribution system [11]. This has motivated the development of heuristic methods combining load flow calculations [13], [14], machine learning functions [12] or pattern-based load allocation [15] with ad hoc SE techniques.
What these hybrid schemes generally have in common is a preprocessing phase in which delayed smart meter data or daily load patterns are somehow exploited to generate pseudomeasurements for the SE phase. In the foreseeable future, if not in the near term, smart meter data will be collected and preprocessed by substation-level management systems, at much faster scan rates than those achievable if every piece of information had to be gathered at the centralized DMS. Whereas a DMS is in charge of an entire system, typically serving several million customers, a 60-MW primary substation may serve three orders of magnitude less customers, whose smart meter data are in turn concentrated at less than a hundred intermediate points (generally secondary substations serving the LV subsystem). Having these data collected at the primary substation, at rates ranging from 5 to 20 times an hour, is a feasible choice even with today's bandwidths and technology.
In this context, the substation-level SE tool will have to deal with two heterogeneous types of information, as explained in more detail further below:
1) regular SCADA measurements, and eventually those coming from new smart grid sensors, captured every few seconds;
2) smart meter (or smart meter concentrator) readings and distributed generation production, updated every few minutes.
This naturally leads to an information processing model in two time scales. Even though two-time-scale problems have long been known and exploited in several engineering fields (see for instance [16]-[19]), including SE of chemical or biological processes [20], to the inventor's knowledge such a notion has not been explored so far in power system SE.